Handling multiblock data in wine authenticity by sequentially orthogonalized one class partial least squares

Adriano A Gomes; Liudmyla Khvalbota; Larisa Onça; Andrea Machyňáková; Ivan Špánik

doi:10.1016/j.foodchem.2022.132271

Handling multiblock data in wine authenticity by sequentially orthogonalized one class partial least squares

Food Chem. 2022 Jul 15:382:132271. doi: 10.1016/j.foodchem.2022.132271. Epub 2022 Jan 30.

Authors

Adriano A Gomes¹, Liudmyla Khvalbota², Larisa Onça³, Andrea Machyňáková², Ivan Špánik⁴

Affiliations

¹ Institute of Chemistry, Federal University of Rio Grande do Sul, Bento Gonçalves Avenue, 9500, 91501-970 Porto Alegre, RS, Brazil; Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia. Electronic address: araujo.gomes@ufrgs.br.
² Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia.
³ Institute of Chemistry, Federal University of Rio Grande do Sul, Bento Gonçalves Avenue, 9500, 91501-970 Porto Alegre, RS, Brazil.
⁴ Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia. Electronic address: ivan.spanik@stuba.sk.

PMID: 35189444
DOI: 10.1016/j.foodchem.2022.132271

Abstract

New approach to deal with food authentication by modelling methods based on data recorded from different sources is proposed and called OC-PLS, combines an orthogonalization step between the different data sets to eliminate redundant information followed by definition of an acceptance area for a target class by OC-PLS. The proposed method was evaluated in two case studies. The first study used a controlled scenario with simulated data. In the second case study, the approach was applied using UV-VIS and IR data, in order to differentiate Slovak Tokaj Selection wines of high quality from other lower market value wines from the Slovak Tokaj wine region. In both cases, better results were reached than when individual blocks of data were achieved. The proposed method proved to be effective in properly exploring common and distinct information in each data block. The best compromise between sensitivity and selectivity in the prediction step was achieved.

Keywords: Multiblock data; One class classification; Tokaj wine.

MeSH terms

Least-Squares Analysis
Slovakia
Wine* / analysis